Osteoarthritis (OA) is a degenerative joint disease that primarily affects the knee, causing cartilage deterioration and discomfort. Early diagnosis is crucial for effective management, as it can slow disease progression and improve the quality of life. This study proposes a deep learning approach to automatically sense and classify knee OA severity from X-ray images using Convolutional Neural Networks (CNNs) and the VGG16 model, establishing X-ray imaging as a biomedical sensing modality for automated disease grading. The models were trained on a dataset of knee X-ray images, and performance was evaluated using accuracy, precision, recall, and F1-score. The proposed CNNs model achieved 99%... More >

Sentiment analysis is a crucial component of intelligent information processing systems, enabling machines to understand and categorize human opinions expressed in text. While extensively studied for high-resource languages such as English and Chinese, it remains underexplored for low-resource languages like Urdu. This paper presents an intelligent multilingual sentiment analysis framework for Urdu text by integrating supervised machine learning techniques with a transformer-based model. We manually annotated and preprocessed a dataset collected from various Urdu blog websites, categorizing sentiments into positive, neutral, and negative classes. Four machine learning classifiers—Support V... More >